Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens

Simple item page
datacite.creatorLópez Cortés, Xaviera A.
datacite.creatorManríquez Troncoso, José M.
datacite.creatorYáñez Sepúlveda, Alejandra
datacite.creatorSuazo Soto, Patricio Maximiliano
datacite.date.issued2025
datacite.identifierDOI
datacite.identifier.doi10.3390/ijms26031140
datacite.identifier.issn1422-0067
datacite.identifier.orcid0000-0002-7514-8777
datacite.identifier.orcid0000-0002-9214-5666
datacite.identifier.wosidWOS:001418588700001
datacite.rightsAcceso abierto
datacite.subjectAntibiotic resistance
datacite.subjectStaphylococcus aureus
datacite.subjectEscherichia coli
datacite.subjectKlebsiella pneumoniae
datacite.subjectMachine learning
datacite.subjectTransfer learning
datacite.titleIntegrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens
dc.date.accessioned2025-03-05T14:55:20Z
dc.date.available2025-03-05T14:55:20Z
dc.description.abstractAntimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of mass spectral data generated by VITEK® MS instruments for predicting antibiotic resistance in Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae using machine learning algorithms. Additionally, the potential of pre-trained models was assessed through transfer learning analysis. A dataset comprising 2229 mass spectra was collected, and classification algorithms, including Support Vector Machines, Random Forest, Logistic Regression, and CatBoost, were applied to predict resistance. CatBoost demonstrated a clear advantage over the other models, effectively handling complex non-linear relationships within the spectra and achieving an AUROC of 0.91 and an F1 score of 0.78 for E. coli. In contrast, transfer learning yielded suboptimal results. These findings highlight the potential of gradient-boosting techniques to enhance resistance prediction, particularly with data from less conventional platforms like VITEK® MS. Furthermore, the identification of specific biomarkers using SHAP values indicates promising potential for clinical applications in early diagnosis. Future efforts focused on standardizing data and refining algorithms could expand the utility of these approaches across diverse clinical environments, supporting the global fight against AMR.
dc.description.pages13 p.
dc.identifier.folio11220897
dc.identifier.urihttps://repositorio.utalca.cl/repositorio/handle/1950/15458
dc.languageInglés
dc.publisherMdpi
dc.relation.urihttps://www.mdpi.com/1422-0067/26/3/1140
dc.sourceInternational Journal of Molecular Sciences
oaire.citationTitleInternational Journal of Molecular Sciences
oaire.fundingReferenceThe authors declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by ANID Chile, FONDECYT Iniciación en Investigación No. 11220897.
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.licenseCondition.urihttps://creativecommons.org/licenses/by/4.0/
oaire.resourceTypeArtículo de Revista
oaire.versionVersión Publicada
utalca.catalogadorPAG
utalca.facultadUniversidad de Talca (Chile). Facultad de Ciencias de la Salud. Departamento de Microbiología.
utalca.facultadUniversidad de Talca (Chile). Centro de Ecología Integrativa.
utalca.idcargapag050325
utalca.indexArtículo indexado en Web of Science
utalca.indexArtículo indexado en Scopus
utalca.informaciondegeneroHombre y Mujer
utalca.odsSalud y bienestar
utalca.odsIndustria, innovación e infraestructura
utalca.odsReducción de las desigualdades
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0403251315.pdf
Size:
967.61 KB
Format:
Adobe Portable Document Format
Download
Collections
10.1. Artículos de revistas